Interference mitigation in wireless communication using artificial interference signal

ABSTRACT

Methods, devices, and system related to wireless communications are disclosed. In one example aspect, a device for wireless communication includes a processor that is configured to determine an estimation of an interference signal for a connection between the device and a receiving device in a wireless communication system, construct an interference elimination signal based on the estimation of the interference signal, and perform a data transmission to the receiving device with the interference elimination signal to enable the receiving device to eliminate the interference signal in the data transmission. The estimation of the interference signal is determined by building a probabilistic model of the interference signal using at least an interference template associate with a characteristic of the device or one or more measurements of a channel condition collected within a predefined observation window.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/384,619, filed on Jul. 23, 2021, entitled INTERFERENCE MITIGATION INWIRELESS COMMUNICATION USING ARTIFICIAL INTERFERENCE SIGNAL, which ishereby incorporated by reference in its entirety.

BACKGROUND

In telecommunications, an interference refers to the addition ofunwanted signals to a useful signal that modifies a signal as it travelsalong a communication channel between the transmitter and receiver.Multiple types of interference exist in telecommunications, such asinter-symbol interference, inter-carrier interference, andadjacent-channel interference. Interference often affectstelecommunications in a disruptive manner.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed descriptions of implementations of the present invention willbe described and explained through the use of the accompanying drawings.

FIG. 1 is a block diagram that illustrates a wireless communicationssystem.

FIG. 2 illustrates an example of inter-cell interference andinterference caused by nearby powerlines.

FIG. 3 illustrates an example application of an interference eliminationsignal in accordance with one or more embodiments of the presenttechnology.

FIG. 4 is a flow chart representation of a method for wirelesscommunication in accordance with one or more embodiments of the presenttechnology.

FIG. 5 is a block diagram that illustrates an example of a computersystem in which at least some operations described herein can beimplemented.

The technologies described herein will become more apparent to thoseskilled in the art from studying the Detailed Description in conjunctionwith the drawings. Embodiments or implementations describing aspects ofthe invention are illustrated by way of example, and the same referencescan indicate similar elements. While the drawings depict variousimplementations for the purpose of illustration, those skilled in theart will recognize that alternative implementations can be employedwithout departing from the principles of the present technologies.Accordingly, while specific implementations are shown in the drawings,the technology is amenable to various modifications.

DETAILED DESCRIPTION

Wireless transmissions are exposed to various channel conditions subjectto interference and path loss caused by neighboring cell transmissions,cross-channel interference, fading, obstacles, and various otherspurious transmissions. In cellular communications, interference isconventionally addressed by increasing power levels of transmissionsand/or allocating more resources in areas that have poor coverages. Withthe advance of wireless technologies, especially for high-frequencytransmissions, there remains a need to improve the handling ofinterference while conserving power consumption and resource allocation.

This patent document discloses techniques that can be implemented as aninterference modeling and mitigation framework applied in variousembodiments of wireless communications to estimate the interferencesignals and transmit interference canceling signals, thereby mitigatingthe impact of interference signals. The interference modeling andmitigation framework can determine a baseline interference templatebased on the location of the device and collect measurement results thatare representative of the channel or network conditions. A statisticallearning model can be used to adapt the baseline interference templatebased on the measurement results and generate an interferenceelimination signal that can be applied to minimize or eliminate theactual interference present in the communications.

Wireless Communications System

FIG. 1 is a block diagram that illustrates a wireless telecommunicationsystem 100 (“system 100”) in which aspects of the disclosed technologyare incorporated. The system 100 includes base stations 102-1 through102-4 (also referred to individually as “base station 102” orcollectively as “base stations 102”). A base station is a type ofnetwork access node (NAN) that can also be referred to as a cell site, abase transceiver station, or a radio base station. The system 100 caninclude any combination of NANs including an access point, radiotransceiver, gNodeB (gNB), NodeB, eNodeB (eNB), Home NodeB or eNodeB, orthe like. In addition to being a WWAN base station, a NAN can be a WLANaccess point, such as an Institute of Electrical and ElectronicsEngineers (IEEE) 802.11 access point.

The NANs of a network formed by the system 100 also include wirelessdevices 104-1 through 104-8 (referred to individually as “wirelessdevice 104” or collectively as “wireless devices 104”) and a corenetwork 106. The wireless devices 104-1 through 104-8 can correspond toor include network entities capable of communication using variousconnectivity standards. For example, a 5G communication channel can usemillimeter wave (mmW) access frequencies of 28 GHz or more. In someimplementations, the wireless device 104 can operatively couple to abase station 102 over a Long-Term Evolution (LTE)/LTE Advanced (LTE-A)communication channel, which is referred to as a 4G communicationchannel. In some implementations, the base station 102 can providenetwork access to a Fifth-Generation (5G) communication channel.

The core network 106 provides, manages, and controls security services,user authentication, access authorization, tracking, Internet Protocol(IP) connectivity, and other access, routing, or mobility functions. Thebase stations 102 interface with the core network 106 through a firstset of backhaul links 108 (e.g., 51 interfaces) and can perform radioconfiguration and scheduling for communication with the wireless devices104 or can operate under the control of a base station controller (notshown). In some examples, the base stations 102 can communicate, eitherdirectly or indirectly (e.g., through the core network 106), with eachother over a second set of backhaul links 110-1 through 110-3 (e.g., X1interfaces), which can be wired or wireless communication links.

The base stations 102 can wirelessly communicate with the wirelessdevices 104 via one or more base station antennas. The cell sites canprovide communication coverage for geographic coverage areas 112-1through 112-4 (also referred to individually as “coverage area 112” orcollectively as “coverage areas 112”). The geographic coverage area 112for a base station 102 can be divided into sectors making up only aportion of the coverage area (not shown). The system 100 can includebase stations of different types (e.g., macro and/or small cell basestations). In some implementations, there can be overlapping geographiccoverage areas 112 for different service environments (e.g.,Internet-of-Things (IoT), mobile broadband (MBB), vehicle-to-everything(V2X), machine-to-machine (M2M), machine-to-everything (M2X),ultra-reliable low-latency communication (URLLC), machine-typecommunication (MTC)), etc.

The system 100 can include a 5G network and/or an LTE/LTE-A or othernetwork. In an LTE/LTE-A network, the term eNB is used to describe thebase stations 102 and in 5G new radio (NR) networks, the term gNBs isused to describe the base stations 102 that can include mmWcommunications. The system 100 can thus form a heterogeneous network inwhich different types of base stations provide coverage for variousgeographical regions. For example, each base station 102 can providecommunication coverage for a macro cell, a small cell, and/or othertypes of cells. As used herein, the term “cell” can relate to a basestation, a carrier or component carrier associated with the basestation, or a coverage area (e.g., sector) of a carrier or base station,depending on context.

A macro cell generally covers a relatively large geographic area (e.g.,several kilometers in radius) and can allow access by wireless deviceswith service subscriptions with a wireless network service provider. Asindicated earlier, a small cell is a lower-powered base station, ascompared with a macro cell, and can operate in the same or different(e.g., licensed, unlicensed) frequency bands as macro cells. Examples ofsmall cells include pico cells, femto cells, and micro cells. Ingeneral, a pico cell can cover a relatively smaller geographic area andcan allow unrestricted access by wireless devices with servicesubscriptions with the network provider. A femto cell covers arelatively smaller geographic area (e.g., a home) and can providerestricted access by wireless devices having an association with thefemto cell (e.g., wireless devices in a closed subscriber group (CSG),wireless devices for users in the home). A base station can support oneor multiple (e.g., two, three, four, and the like) cells (e.g.,component carriers). All fixed transceivers noted herein that canprovide access to the network are NANs, including small cells.

The communication networks that accommodate various disclosed examplescan be packet-based networks that operate according to a layeredprotocol stack. In the user plane, communications at the bearer orPacket Data Convergence Protocol (PDCP) layer can be IP-based. A RadioLink Control (RLC) layer then performs packet segmentation andreassembly to communicate over logical channels. A Medium Access Control(MAC) layer can perform priority handling and multiplexing of logicalchannels into transport channels. The MAC layer can also use Hybrid ARQ(HARQ) to provide retransmission at the MAC layer, to improve linkefficiency. In the control plane, the Radio Resource Control (RRC)protocol layer provides establishment, configuration, and maintenance ofan RRC connection between a wireless device 104 and the base stations102 or core network 106 supporting radio bearers for the user planedata. At the Physical (PHY) layer, the transport channels are mapped tophysical channels.

As illustrated, the wireless devices 104 are distributed throughout thesystem 100, where each wireless device 104 can be stationary or mobile.A wireless device can be referred to as a mobile station, a subscriberstation, a mobile unit, a subscriber unit, a wireless unit, a remoteunit, a handheld mobile device, a remote device, a mobile subscriberstation, an access terminal, a mobile terminal, a wireless terminal, aremote terminal, a handset, a mobile client, a client, or the like.Examples of a wireless device include user equipment (UE) such as amobile phone, a personal digital assistant (PDA), a wireless modem, ahandheld mobile device (e.g., wireless devices 104-1 and 104-2), atablet computer, a laptop computer (e.g., wireless device 104-3), awearable (e.g., wireless device 104-4). A wireless device can beincluded in another device such as, for example, a drone (e.g., wirelessdevice 104-5), a vehicle (e.g., wireless device 104-6), an augmentedreality/virtual reality (AR/VR) device such as a head-mounted displaydevice (e.g., wireless device 104-7), an IoT device such as an appliancein a home (e.g., wireless device 104-8), a portable gaming console, or awirelessly connected sensor that provides data to a remote server over anetwork.

A wireless device can communicate with various types of base stationsand network equipment at the edge of a network including macroeNBs/gNBs, small cell eNBs/gNBs, relay base stations, and the like. Awireless device can also communicate with other wireless devices eitherwithin or outside the same coverage area of a base station viadevice-to-device (D2D) communications.

The communication links 114-1 through 114-11 (also referred toindividually as “communication link 114” or collectively as“communication links 114”) shown in system 100 include uplink (UL)transmissions from a wireless device 104 to a base station 102, and/ordownlink (DL) transmissions, from a base station 102 to a wirelessdevice 104. The downlink transmissions can also be called forward linktransmissions while the uplink transmissions can also be called reverselink transmissions. Each communication link 114 includes one or morecarriers, where each carrier can be a signal composed of multiplesub-carriers (e.g., waveform signals of different frequencies) modulatedaccording to the various radio technologies. Each modulated signal canbe sent on a different sub-carrier and carry control information (e.g.,reference signals, control channels), overhead information, user data,etc. The communication links 114 can transmit bidirectionalcommunications using FDD (e.g., using paired spectrum resources) or TDDoperation (e.g., using unpaired spectrum resources). In someimplementations, the communication links 114 include LTE and/or mmWcommunication links.

In some implementations of the system 100, the base stations 102 and/orthe wireless devices 104 include multiple antennas for employing antennadiversity schemes to improve communication quality and reliabilitybetween base stations 102 and wireless devices 104. Additionally, oralternatively, the base stations 102 and/or the wireless devices 104 canemploy multiple-input, multiple-output (MIMO) techniques that can takeadvantage of multi-path environments to transmit multiple spatial layerscarrying the same or different coded data.

Interference Modeling

Interference in real world is unpredictable. FIG. 2 illustrates anexample of inter-cell interference 204 and interference caused by nearbypowerlines 206. Unlike hardware-based interferences (e.g., interferencescaused by hardware variations in gateways or backbone devices), which isoften modeled as white Gaussian interference, environmental interferencedoes not follow standard probability distributions very well.Furthermore, environmental interference is subject to change. Whenterminal devices move across different geographical locations orterrains, the associated interference patterns change at the same time.Therefore, static modeling of interference/interference based onstandard probability distributions is not sufficient to provide accurateestimation of the real-world interference.

Instead of relying on static characterization of interference and/orinterference in wireless communication channels, the inventors haveformulated techniques for dynamic modeling of interference based onstatistical modeling techniques. For example, an interference modelingand mitigation framework can be implemented to build a non-Gaussianprobability distribution based on past observations of channelconditions or interference values. To construct the probabilitydistribution, the interference modeling and mitigation framework canfirst consider multiple factors, such as the geographical location of aterminal device and/or the operating spectrum of the terminal device.

Signal transmissions can be heavily impacted by the landscape associatedwith the geographical location of the terminal device or the basestation. For example, the topology associated with the geographicallocation, e.g., whether the device is located in urban centers with lotsof buildings, a suburban flat land, or a hilltop with many trees, canintroduce different interference in the signal transmissions. Additionalfactors, such as spurious transmissions from nearby power lines or othertypes of transmission signals, can also impact the interferencepresented in the wireless communication signals. The interferencemodeling and mitigation framework can provide baseline interferencetemplates according to the location, topology, and/or landscapeassociated with the devices. For example, templates based ongeographical locations can be pre-determined based on simulation or realinterference data collected in the areas. Geographical topology variesfrom one location to another. As an example, Seattle area has mountains,lakes and tall trees, while Dallas has a relatively flat terrain.Different terrain characteristics cause different signal propagationpatterns. Accordingly, a specific baseline template can be designed forSeattle as compared to Dallas. Once a connection is established betweena terminal device and a base station, an appropriate baseline templateis selected according to the location of the devices.

The baseline template can also be further adapted dynamically based onthe conditions or characteristics of the connection. When the terminaldevice establishes a communication connection with the base station, itoften measures the channel conditions using certain reference signalssuch that transmission configurations can be adjusted when the channelconditions are suboptimal. For example, in cellular communicationsystems, such as the Long-Term Evolution (LTE) systems or the New Radio(NR) systems, the terminal device or the base station performsmeasurements of the channel conditions in the spectrum that it operateson using several reference signals (e.g., channel state informationreference signals and/or sounding reference signals). The terminaldevice also reports the measurements, such as Channel Quality Indicator(CQI), the Precoding Matrix Index (PMI), and Rank Indicator (RI), to thebase station so that the base station can adjust transmissionconfigurations if necessary. In some embodiments, in addition tomeasurements based on existing reference signals, new types of referencesignals can be specifically designed to the wireless communication tofacilitate the modeling of interference. The measurement results can beused by both the base station and the terminal device to further refinethe baseline interference templates. Details regarding refining thebaseline interference templates are further discussed below.

In some embodiments, the interference modeling and mitigation frameworkdefines an observation window for collecting the measurement resultsbefore providing estimated interference patterns. In the observationwindow, the interference modeling and mitigation framework collects themeasurement results such as the signal-to-interference ratio and/or thereceived signal strength indicator and adopts statistical modelingtechniques to obtain one or more probability distribution functions thatrepresent the dominant interference signals. For example, a probabilitydistribution function that represents the decibel values of the dominantsinusoidal interference signal can be constructed. In case of acontinuous random variable, the probability of X in an interval (a, b)can be calculated.

P(a<X<b)=∫_(a) ^(b) f(x)   Eq. (1)

In some embodiments, probabilistic modeling tools, such as the closestpattern matching (CPM) model in which each data value is a probabilitydistribution function of the observed interference values, can betrained based on N previous measurement values observed in theobservation window to further refine the baseline interference template.The closest pattern matching (CPM) model performs well in modeling theinterference or interference for heavy traffic environments withsufficient number of measurement samples. For low traffic environments,probabilistic modeling tools such as the correlation distortion modelcan be applied to generate a non-Gaussian random process with anauto-correlation function to transform the non-Gaussian to Gaussiandistribution. The correlation distortion method is also suitable forinterferences that have long-term correlation (e.g., periodicinterferences that have long periodicities).

In some embodiments, the baseline interreference template can includeinformation about characteristic of the traffic flow associated with aparticular geographical location, thereby allowing the terminal deviceor the base station to select appropriate statistical modeling tools tofurther refine the estimated interference.

A longer observation window having more measurement results can lead tomore accurate estimation of the interference patterns. However, longobservation windows can also introduce computational complexity, therebyincreasing power consumption of the devices. Therefore, an appropriateobservation window can be selected based on the computational power ofthe device to achieve a desirable balance between the accuracy of theinterference model and the amount of required power or computationresources. The length of the observation window can also be determinedaccording to vendors and mobile operators' needs on accuracyrequirements. For example, longer observation windows lead to betteraccuracy. However, the overhead can also hurt overall serviceperformance. In some embodiments, the mobile operator or handsetmanufacturer can extend the observation window length based on theprocessing load such that no performance loss is incurred. Theobservation window can be represented in a number of hours, days, weeks,months, quarters, and so on. In some embodiments, an observation windowof a week is used for the terminal and base station. In someembodiments, the observation window can be increased to a month fordevices that have sufficient computing power.

Interference Mitigation

After the probabilistic model is trained based on the channelmeasurements collected in the observation window, the interferencemodeling and mitigation framework can use the probability model (e.g.,the CPM model, the correlation distortion model, or other probabilitymodels) to generate an interference elimination signal. In someembodiments, the interference elimination signal is a signal that hasthe same amplitude as the estimated interference signal but has anopposite phase (e.g., 180-degree phase shift). As shown in FIG. 3 , adesired signal 301 can be interfered with an interference signal 303.After applying the interference elimination signal 305, which has anopposite phase as compared to the estimation generated by theprobabilistic model, the actual interference is largely eliminatedwithout the need to change transmit power or adjust allocatedtransmission resources.

In some embodiments, the receiving end can derive the interferenceelimination signal based on its own geographical location and/ormeasurement results and apply the interference elimination signal toincoming transmissions. In some embodiments, the transmitting end caninclude the interference elimination signal as a non-payload portion ofthe transmission to enable the receiving end to obtain the desiredsignal more effectively. For example, the interference eliminationsignal can be transmitted as a part of the control signal in a controlchannel that corresponds to the data channel for data transmissions.After receiving the interference elimination signal by the receivingend, the receiving end can apply the interference elimination signal todata transmission on the data channel(s).

FIG. 4 is a flowchart representation of a method 400 for wirelesscommunication in accordance with one or more embodiments of the presenttechnology. The method 400 includes, at operation 410, determining, by atransmitting device, an estimation of an interference signal between thetransmitting device and a receiving device based on probabilistic modelof the interference signal constructed using at least a templateassociated with a characteristic of the transmitting device or one ormore measurements of a channel condition collected within a predefinedobservation window. The method 400 also includes, at operation 420,performing, by the transmitting device, a data transmission to thereceiving device based on the estimation of the interference signal.

In some embodiments, performing the data transmission comprisestransmitting, by the transmitting device, an interference eliminationsignal that has a same amplitude as the estimation of the interferencesignal. A phase of the interference elimination signal has a 180-degreeshift as compared to a phase of the estimation of the interferencesignal.

In some embodiments, the interference elimination signal is transmittedas a non-payload portion of the data transmission. In some embodiments,the template is determined based on the characteristic of thetransmitting device, such as a geographical location of the transmittingdevice, a landscaping near the geographical location of the transmittingdevice, or a topology associated with the geographical location of thetransmitting device. In some embodiments, the predefined observationwindow is represented as a number of days, weeks, or months.

It is appreciated that, the techniques disclosed herein can be used toenable dynamical modeling of interference signals based on geometriccharacteristics and/or channel conditions, thereby allowing moreaccurate elimination of the undesired interference is communicationsystems.

Computer System

FIG. 5 is a block diagram that illustrates an example of a computersystem 500 in which at least some operations described herein can beimplemented. As shown, the computer system 500 can include: one or moreprocessors 502, main memory 506, non-volatile memory 510, a networkinterface device 512, video display device 518, an input/output device520, a control device 522 (e.g., keyboard and pointing device), a driveunit 524 that includes a storage medium 526, and a signal generationdevice 530 that are communicatively connected to a bus 516. The bus 516represents one or more physical buses and/or point-to-point connectionsthat are connected by appropriate bridges, adapters, or controllers.Various common components (e.g., cache memory) are omitted for brevity.Instead, the computer system 500 is intended to illustrate a hardwaredevice on which components illustrated or described relative to theexamples of the figures and any other components described in thisspecification can be implemented.

The computer system 500 can take any suitable physical form. Forexample, the computing system 500 can share a similar architecture asthat of a server computer, personal computer (PC), tablet computer,mobile telephone, game console, music player, wearable electronicdevice, network-connected (“smart”) device (e.g., a television or homeassistant device), AR/VR systems (e.g., head-mounted display), or anyelectronic device capable of executing a set of instructions thatspecify action(s) to be taken by the computing system 500. In someimplementation, the computer system 500 can be an embedded computersystem, a system-on-chip (SOC), a single-board computer system (SBC) ora distributed system such as a mesh of computer systems or include oneor more cloud components in one or more networks. Where appropriate, oneor more computer systems 700 can perform operations in real-time, nearreal-time, or in batch mode.

The network interface device 512 enables the computing system 500 tomediate data in a network 514 with an entity that is external to thecomputing system 500 through any communication protocol supported by thecomputing system 500 and the external entity. Examples of the networkinterface device 512 include a network adaptor card, a wireless networkinterface card, a router, an access point, a wireless router, a switch,a multilayer switch, a protocol converter, a gateway, a bridge, bridgerouter, a hub, a digital media receiver, and/or a repeater, as well asall wireless elements noted herein.

The memory (e.g., main memory 506, non-volatile memory 510,machine-readable medium 526) can be local, remote, or distributed.Although shown as a single medium, the machine-readable medium 526 caninclude multiple media (e.g., a centralized/distributed database and/orassociated caches and servers) that store one or more sets ofinstructions 528. The machine-readable (storage) medium 526 can includeany medium that is capable of storing, encoding, or carrying a set ofinstructions for execution by the computing system 500. Themachine-readable medium 526 can be non-transitory or comprise anon-transitory device. In this context, a non-transitory storage mediumcan include a device that is tangible, meaning that the device has aconcrete physical form, although the device can change its physicalstate. Thus, for example, non-transitory refers to a device remainingtangible despite this change in state.

Although implementations have been described in the context of fullyfunctioning computing devices, the various examples are capable of beingdistributed as a program product in a variety of forms. Examples ofmachine-readable storage media, machine-readable media, orcomputer-readable media include recordable-type media such as volatileand non-volatile memory devices 910, removable flash memory, hard diskdrives, optical disks, and transmission-type media such as digital andanalog communication links.

In general, the routines executed to implement examples herein can beimplemented as part of an operating system or a specific application,component, program, object, module, or sequence of instructions(collectively referred to as “computer programs”). The computer programstypically comprise one or more instructions (e.g., instructions 504,508, 928) set at various times in various memory and storage devices incomputing device(s). When read and executed by the processor 502, theinstruction(s) cause the computing system 500 to perform operations toexecute elements involving the various aspects of the disclosure.

Remarks

The description and associated drawings are illustrative examples andare not to be construed as limiting. This disclosure provides certaindetails for a thorough understanding and enabling description of theseexamples. One skilled in the relevant technology will understand,however, that the invention can be practiced without many of thesedetails. Likewise, one skilled in the relevant technology willunderstand that the invention can include well-known structures orfeatures that are not shown or described in detail, to avoidunnecessarily obscuring the descriptions of examples.

The terms “example”, “embodiment” and “implementation” are usedinterchangeably. For example, reference to “one example” or “an example”in the disclosure can be, but not necessarily are, references to thesame implementation; and, such references mean at least one of theimplementations. The appearances of the phrase “in one example” are notnecessarily all referring to the same example, nor are separate oralternative examples mutually exclusive of other examples. A feature,structure, or characteristic described in connection with an example canbe included in another example of the disclosure. Moreover, variousfeatures are described which can be exhibited by some examples and notby others. Similarly, various requirements are described which can berequirements for some examples but no other examples.

The terminology used herein should be interpreted in its broadestreasonable manner, even though it is being used in conjunction withcertain specific examples of the invention. The terms used in thedisclosure generally have their ordinary meanings in the relevanttechnical art, within the context of the disclosure, and in the specificcontext where each term is used. A recital of alternative language orsynonyms does not exclude the use of other synonyms. Specialsignificance should not be placed upon whether or not a term iselaborated or discussed herein. The use of highlighting has no influenceon the scope and meaning of a term. Further, it will be appreciated thatthe same thing can be said in more than one way.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense; that is to say, in the sense of“including, but not limited to.” As used herein, the terms “connected,”“coupled,” or any variant thereof means any connection or coupling,either direct or indirect, between two or more elements; the coupling orconnection between the elements can be physical, logical, or acombination thereof. Additionally, the words “herein,” “above,” “below,”and words of similar import can refer to this application as a whole andnot to any particular portions of this application. Where contextpermits, words in the above Detailed Description using the singular orplural number may also include the plural or singular numberrespectively. The word “or” in reference to a list of two or more itemscovers all of the following interpretations of the word: any of theitems in the list, all of the items in the list, and any combination ofthe items in the list. The term “module” refers broadly to softwarecomponents, firmware components, and/or hardware components.

While specific examples of technology are described above forillustrative purposes, various equivalent modifications are possiblewithin the scope of the invention, as those skilled in the relevant artwill recognize. For example, while processes or blocks are presented ina given order, alternative implementations can perform routines havingsteps, or employ systems having blocks, in a different order, and someprocesses or blocks may be deleted, moved, added, subdivided, combined,and/or modified to provide alternative or sub-combinations. Each ofthese processes or blocks can be implemented in a variety of differentways. Also, while processes or blocks are at times shown as beingperformed in series, these processes or blocks can instead be performedor implemented in parallel, or can be performed at different times.Further, any specific numbers noted herein are only examples such thatalternative implementations can employ differing values or ranges.

Details of the disclosed implementations can vary considerably inspecific implementations while still being encompassed by the disclosedteachings. As noted above, particular terminology used when describingfeatures or aspects of the invention should not be taken to imply thatthe terminology is being redefined herein to be restricted to anyspecific characteristics, features, or aspects of the invention withwhich that terminology is associated. In general, the terms used in thefollowing claims should not be construed to limit the invention to thespecific examples disclosed herein, unless the above DetailedDescription explicitly defines such terms. Accordingly, the actual scopeof the invention encompasses not only the disclosed examples, but alsoall equivalent ways of practicing or implementing the invention underthe claims. Some alternative implementations can include additionalelements to those implementations described above or include fewerelements.

Any patents and applications and other references noted above, and anythat may be listed in accompanying filing papers, are incorporatedherein by reference in their entireties, except for any subject matterdisclaimers or disavowals, and except to the extent that theincorporated material is inconsistent with the express disclosureherein, in which case the language in this disclosure controls. Aspectsof the invention can be modified to employ the systems, functions, andconcepts of the various references described above to provide yetfurther implementations of the invention.

To reduce the number of claims, certain implementations are presentedbelow in certain claim forms, but the applicant contemplates variousaspects of an invention in other forms. For example, aspects of a claimcan be recited in a means-plus-function form or in other forms, such asbeing embodied in a computer-readable medium. A claim intended to beinterpreted as a mean-plus-function claim will use the words “meansfor.” However, the use of the term “for” in any other context is notintended to invoke a similar interpretation. The applicant reserves theright to pursue such additional claim forms in either this applicationor in a continuing application.

We claim:
 1. A method for wireless communication, comprising:transmitting, by a communication device to a network node, a measurementresult associated with a channel between the network node and thecommunication device to enable an estimation of an interference signalbased on a probabilistic model that is constructed using at least themeasurement result; determining, by the communication device, aninterference elimination signal that has a substantially same amplitudeas the estimated interference signal and a substantially opposite phaseas compared to the estimated interference signal; and applying, by thecommunication device, the interference elimination signal to a datatransmission to the network node.
 2. The method of claim 1, wherein thedetermining of the interference elimination signal comprises: receiving,by the communication device, an indication of the interferenceelimination signal from the network node, wherein the interferenceelimination signal is determined by the network node based on theprobabilistic model.
 3. The method of claim 2, wherein the indication iscarried in a control signal in a control channel that corresponds to adata channel for the data transmission.
 4. The method of claim 1,wherein the determining of the interference elimination signalcomprises: deriving, by the communication device, the interferenceelimination signal based on a geographical location of the communicationdevice.
 5. The method of claim 4, wherein the interference eliminationsignal is derived based on an interference template associated with acharacteristic of the geographical location of the communication device,the characteristic comprising a landscape near the geographical locationor a topology associated with the geographical location.
 6. The methodof claim 5, further comprising: refining, by the communication device,the interference template based on at least the measurement resultcollected in a predefined time window.
 7. The method of claim 1, whereinthe measurement result comprises at least one of: a Channel QualityIndicator (CQI), a Precoding Matrix Index (PMI), or Rank Indicator (RI).8. A device for wireless communication, comprising one or moreprocessors that are configured to perform operations comprising:transmitting, to a network node, a measurement result associated with achannel between the network node and the device to enable an estimationof an interference signal based on a probabilistic model that isconstructed using at least the measurement result; determining aninterference elimination signal that has a substantially same amplitudeas the estimated interference signal and a substantially opposite phaseas compared to the estimated interference signal; and applying theinterference elimination signal to a transmission to the network node.9. The device of claim 8, wherein the operations comprise: receiving anindication of the interference elimination signal from the network node,wherein the interference elimination signal is determined by the networknode based on the probabilistic model.
 10. The device of claim 9,wherein the indication is carried in control information thatcorresponds to the transmission.
 11. The device of claim 8, wherein theoperations comprise: deriving the interference elimination signal basedon a geographical location of the device.
 12. The device of claim 11,wherein the interference elimination signal is derived based on aninterference template associated with a characteristic of thegeographical location of the device, the characteristic comprising alandscape near the geographical location or a topology associated withthe geographical location.
 13. The device of claim 12, wherein theoperations further comprise: refining the interference template based onat least the measurement result collected in a predefined time window.14. The device of claim 8, wherein the measurement result comprising atleast one of a Channel Quality Indicator (CQI), a Precoding Matrix Index(PMI), or Rank Indicator (RI).
 15. A method for wireless communication,the method comprising: estimating, by a network node, an interferencesignal based on a statistical model associated with a channel betweenthe network node and a communication device, wherein the statisticalmodel is based on one or more measurements of the channel; determining,by the network node, an interference elimination signal based on theestimated interference signal, wherein the interference eliminationsignal has a substantially same amplitude as the estimated interferencesignal and a substantially opposite phase as compared to the estimatedinterference signal; and indicating the interference elimination signalto the communication device to enable the communication device toeliminate the interference signal in a subsequent transmission.
 16. Themethod of claim 15, comprising: receiving, by the network node, the oneor more measurements of the channel from the communication device in atime window.
 17. The method of claim 16, wherein the time window isrepresented in a number of hours, days, weeks, months, or quarters. 18.The method of claim 15, comprising: constructing the statistical modelusing a template associated with a characteristic of the network node orthe communication device.
 19. The method of claim 18, wherein thecharacteristic comprises a geographical location of the network node orthe communication device, a landscape near the geographical location ofthe network node or the communication device, or a topology associatedwith the geographical location of the network node or the communicationdevice.
 20. The method of claim 15, wherein the indicating of theinterference elimination signal is carried in control information forthe subsequent transmission.